Versioned weights
Organize checkpoint references, artifact hashes, architecture details and training metadata in a consistent record.
PROJECT PREVIEW · IN DEVELOPMENT
Every checkpoint.
Every change.
A traceable trajectory.
BEFORET explores a structured way to record AI model weight versions, follow checkpoint lineage and understand how models evolve across training, fine-tuning and merging.
项目预览 · 开发中
每个检查点。
每一次变化。
清晰可追溯的演化轨迹。
BEFORET 探索一种结构化的方法，用于记录 AI 模型权重的版本、追踪检查点谱系，并理解模型在训练、微调与合并过程中的变化。
WEIGHT LINEAGE / CHECKPOINT GRAPHILLUSTRATION · 示意图
A conceptual lineage graph, not live telemetry. A trajectory represents a recorded sequence of model artifacts and experiments.概念性谱系示意图，非实时遥测数据。轨迹表示模型产物与实验的记录序列。
Proposed scope
From isolated files to connected model history.
Checkpoint lineage
Connect base models, fine-tunes, adapters and merges. Preserve the provenance behind each derived artifact.
Evolution trajectories
Relate weight-change summaries to experiment settings and evaluation results, with attention to compatibility and context.
规划方向
从独立文件到相互关联的模型历史。
权重版本管理
以一致的结构组织检查点引用、文件哈希、模型架构信息及训练元数据。
检查点谱系
关联基础模型、微调版本、适配器与合并产物，保留每个衍生版本的来源信息。
模型演化轨迹
结合兼容性与实验背景，将权重变化摘要与实验设置、评估结果关联起来。
Evidence before interpretation.
A weight difference alone does not establish a capability improvement. The project concept separates artifact identity, training lineage and measured evaluation outcomes.
先记录证据，再进行解释。
权重差异本身并不能证明模型能力有所提升。项目构想将产物标识、训练谱系与实际评估结果分开记录。
Early-stage project. This is a static concept page. No model uploads, account registration, live tracking or public API are available here. All diagrams are illustrative. No analytics or third-party scripts are included.
项目处于早期阶段。本页为静态概念展示，暂不提供模型上传、账户注册、实时追踪或公共 API。所有图表均为示意。本页不包含分析工具或第三方脚本。